** NLP Using Python: - https://www.edureka.co/python-natural-language-processing-course **
This Edureka video will provide you with a comprehensive and detailed knowledge of Natural Language Processing, popularly known as NLP. You will also learn about the different steps involved in processing the human language like Tokenization, Stemming, Lemmatization and much more along with a demo on each one of the topics.
The following topics covered in this video :
1. The Evolution of Human Language
2. What is Text Mining?
3. What is Natural Language Processing?
4. Applications of NLP
5. NLP Components and Demo
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How it Works?
1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each.
2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate!
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About the Course
Edureka's Natural Language Processing using Python Training focuses on step by step guide to NLP and Text Analytics with extensive hands-on using Python Programming Language. It has been packed up with a lot of real-life examples, where you can apply the learnt content to use. Features such as Semantic Analysis, Text Processing, Sentiment Analytics and Machine Learning have been discussed.
This course is for anyone who works with data and text– with good analytical background and little exposure to Python Programming Language. It is designed to help you understand the important concepts and techniques used in Natural Language Processing using Python Programming Language. You will be able to build your own machine learning model for text classification. Towards the end of the course, we will be discussing various practical use cases of NLP in python programming language to enhance your learning experience.
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Who Should go for this course ?
Edureka’s NLP Training is a good fit for the below professionals:
From a college student having exposure to programming to a technical architect/lead in an organisation
Developers aspiring to be a ‘Data Scientist'
Analytics Managers who are leading a team of analysts
Business Analysts who want to understand Text Mining Techniques
'Python' professionals who want to design automatic predictive models on text data
"This is apt for everyone”
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Why Learn Natural Language Processing or NLP?
Natural Language Processing (or Text Analytics/Text Mining) applies analytic tools to learn from collections of text data, like social media, books, newspapers, emails, etc. The goal can be considered to be similar to humans learning by reading such material. However, using automated algorithms we can learn from massive amounts of text, very much more than a human can. It is bringing a new revolution by giving rise to chatbots and virtual assistants to help one system address queries of millions of users.
NLP is a branch of artificial intelligence that has many important implications on the ways that computers and humans interact. Human language, developed over thousands and thousands of years, has become a nuanced form of communication that carries a wealth of information that often transcends the words alone. NLP will become an important technology in bridging the gap between human communication and digital data.
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For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).

Meet the authors of the e-book “From Words To Wisdom”, right here in this webinar on Tuesday May 15, 2018 at 6pm CEST.
Displaying words on a scatter plot and analyzing how they relate is just one of the many analytics tasks you can cover with text processing and text mining in KNIME Analytics Platform.
We’ve prepared a small taste of what text mining can do for you. Step by step, we’ll build a workflow for topic detection, including text reading, text cleaning, stemming, and visualization, till topic detection.
We’ll also cover other useful things you can do with text mining in KNIME. For example, did you know that you can access PDF files or even EPUB Kindle files? Or remove stop words from a dictionary list? That you can stem words in a variety of languages? Or build a word cloud of your preferred politician’s talk? Did you know that you can use Latent Dirichlet Allocation for automatic topic detection? Join us to find out more!
Material for this webinar has been extracted from the e-book “From Words to Wisdom” by Vincenzo Tursi and Rosaria Silipo: https://www.knime.com/knimepress/from-words-to-wisdom
At the end of the webinar, the authors will be available for a Q&A session. Please submit your questions in advance to: [email protected]
This webinar only requires basic knowledge of KNIME Analytics Platform which you can get in chapter one of the KNIME E-Learning Course: https://www.knime.com/knime-introductory-course

Text mining refers to digital social research methods that involve the collection and analysis of unstructured textual data, generally from internet-based sources such as social media and digital archives. In this webinar, Gabe Ignatow and Rada Mihalcea discussed the fundamentals of text mining for social scientists, covering topics including research design, research ethics, Natural Language Processing, the intersection of text mining and text analysis, and tips on teaching text mining to social science students.

Learn more about text mining with R: https://www.datacamp.com/courses/intro-to-text-mining-bag-of-words
Boom, we’re back! You used bag of words text mining to make the frequent words plot. You can tell you used bag of words and not semantic parsing because you didn’t make a plot with only proper nouns. The function didn’t care about word type.
In this section we are going to build our first corpus from 1000 tweets mentioning coffee. A corpus is a collection of documents. In this case, you use read.csv to bring in the file and create coffee_tweets from the text column.
coffee_tweets isn’t a corpus yet though. You have to specify it as your text source so the tm package can then change its class to corpus. There are many ways to specify the source or sources for your corpora. In this next section, you will build a corpus from both a vector and a data frame because they are both pretty common.

Learn more about text mining: https://www.datacamp.com/courses/intro-to-text-mining-bag-of-words
Hi, I'm Ted. I'm the instructor for this intro text mining course. Let's kick things off by defining text mining and quickly covering two text mining approaches.
Academic text mining definitions are long, but I prefer a more practical approach. So text mining is simply the process of distilling actionable insights from text. Here we have a satellite image of San Diego overlaid with social media pictures and traffic information for the roads. It is simply too much information to help you navigate around town. This is like a bunch of text that you couldn’t possibly read and organize quickly, like a million tweets or the entire works of Shakespeare. You’re drinking from a firehose! So in this example if you need directions to get around San Diego, you need to reduce the information in the map. Text mining works in the same way. You can text mine a bunch of tweets or of all of Shakespeare to reduce the information just like this map. Reducing the information helps you navigate and draw out the important features.
This is a text mining workflow. After defining your problem statement you transition from an unorganized state to an organized state, finally reaching an insight. In chapter 4, you'll use this in a case study comparing google and amazon.
The text mining workflow can be broken up into 6 distinct components. Each step is important and helps to ensure you have a smooth transition from an unorganized state to an organized state. This helps you stay organized and increases your chances of a meaningful output.
The first step involves problem definition. This lays the foundation for your text mining project. Next is defining the text you will use as your data. As with any analytical project it is important to understand the medium and data integrity because these can effect outcomes. Next you organize the text, maybe by author or chronologically.
Step 4 is feature extraction. This can be calculating sentiment or in our case extracting word tokens into various matrices. Step 5 is to perform some analysis. This course will help show you some basic analytical methods that can be applied to text. Lastly, step 6 is the one in which you hopefully answer your problem questions, reach an insight or conclusion, or in the case of predictive modeling produce an output.
Now let’s learn about two approaches to text mining. The first is semantic parsing based on word syntax. In semantic parsing you care about word type and order. This method creates a lot of features to study. For example a single word can be tagged as part of a sentence, then a noun and also a proper noun or named entity. So that single word has three features associated with it. This effect makes semantic parsing "feature rich". To do the tagging, semantic parsing follows a tree structure to continually break up the text.
In contrast, the bag of words method doesn’t care about word type or order. Here, words are just attributes of the document. In this example we parse the sentence "Steph Curry missed a tough shot". In the semantic example you see how words are broken down from the sentence, to noun and verb phrases and ultimately into unique attributes.
Bag of words treats each term as just a single token in the sentence no matter the type or order. For this introductory course, we’ll focus on bag of words, but will cover more advanced methods in later courses!
Let’s get a quick taste of text mining!

Natural Language Processing is the task we give computers to read and understand (process) written text (natural language). By far, the most popular toolkit or API to do natural language processing is the Natural Language Toolkit for the Python programming language.
The NLTK module comes packed full of everything from trained algorithms to identify parts of speech to unsupervised machine learning algorithms to help you train your own machine to understand a specific bit of text.
NLTK also comes with a large corpora of data sets containing things like chat logs, movie reviews, journals, and much more!
Bottom line, if you're going to be doing natural language processing, you should definitely look into NLTK!
Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index=1
sample code: http://pythonprogramming.net
http://hkinsley.com
https://twitter.com/sentdex
http://sentdex.com
http://seaofbtc.com

It’s easy to get lost in a lot of text-based data. NVivo is qualitative data analysis software that provides structure to text, helping you quickly unlock insights and make something beautiful to share.
http://www.qsrinternational.com

Heard about Text and Data Mining (TDM) and wondering if it might be a good fit for your research? Find out what text and data mining is and how it can usefully be applied in a research context. Also learn about data sources for text and data mining projects and support, tools, and resources for learning more.

It’s easy to get lost in a lot of text-based data. NVivo is qualitative data analysis software that provides structure to text, helping you quickly unlock insights and make something beautiful to share.
http://www.qsrinternational.com

Please explore free and beautiful Voyant Tools that allow you to perform any text analysis or even mining - word frequency, clouds, co-occurrence (collocations), spider diagrams, context analysis - anything you dreamt of without any prior programming experience or need to buy expensive software.
To those interested in reproducing what we've done and further analyzing comments to Indian political articles (dated March-April and January 2016), please use this link to get the ball rolling:
http://voyant-tools.org/?corpus=0c17d82dbd8b04baae655f90db84a672
Lastly, creators of the video are eternally grateful to our Big Data class professor, who believed in us and kept us going despite any technical or analytical difficulties.

This tutorial will show you how to analyze text data in R. Visit https://deltadna.com/blog/text-mining-in-r-for-term-frequency/ for free downloadable sample data to use with this tutorial. Please note that the data source has now changed from 'demo-co.deltacrunch' to 'demo-account.demo-game'
Text analysis is the hot new trend in analytics, and with good reason! Text is a huge, mainly untapped source of data, and with Wikipedia alone estimated to contain 2.6 billion English words, there's plenty to analyze. Performing a text analysis will allow you to find out what people are saying about your game in their own words, but in a quantifiable manner. In this tutorial, you will learn how to analyze text data in R, and it give you the tools to do a bespoke analysis on your own.

We are now ready to build our first model in RStudio and to do that, we cover:
– Correcting column names derived from tokenization to ensure smooth model training.
– Using caret to set up stratified cross validation.
– Using the doSNOW package to accelerate caret machine learning training by using multiple CPUs in parallel.
– Using caret to train single decision trees on text features and tune the trained model for optimal accuracy.
– Evaluating the results of the cross validation process.
About the Series
This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques:
– Tokenization, stemming, and n-grams
– The bag-of-words and vector space models
– Feature engineering for textual data (e.g. cosine similarity between documents)
– Feature extraction using singular value decomposition (SVD)
– Training classification models using textual data
– Evaluating accuracy of the trained classification models
The data and R code used in this series is available here:
https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Text%20Analytics%20with%20R
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Learn how to perform text analysis with R Programming through this amazing tutorial!
Podcast transcript available here - https://www.superdatascience.com/sds-086-computer-vision/
Natural languages (English, Hindi, Mandarin etc.) are different from programming languages. The semantic or the meaning of a statement depends on the context, tone and a lot of other factors. Unlike programming languages, natural languages are ambiguous.
Text mining deals with helping computers understand the “meaning” of the text. Some of the common text mining applications include sentiment analysis e.g if a Tweet about a movie says something positive or not, text classification e.g classifying the mails you get as spam or ham etc.
In this tutorial, we’ll learn about text mining and use some R libraries to implement some common text mining techniques. We’ll learn how to do sentiment analysis, how to build word clouds, and how to process your text so that you can do meaningful analysis with it.

Download the PDF to keep as reference
http://theexcelclub.com/extract-key-phrases-from-text/
FREE Power BI course - Power BI - The Ultimate Orientation
http://theexcelclub.com/free-excel-training/
Or on Udemy
https://www.udemy.com/power-bi-the-ultimate-orientation
Or on Android App
https://play.google.com/store/apps/details?id=com.PBI.trainigapp
Carry out a text analytics like the big brand...only for free with Power BI and Microsoft Cognitive Services.
this video will cover
Obtain a Text Analytics API Key from Microsoft Cognitive Services
Power BI – Setting up the Text Data
Setting up the Parameter in Power BI
Setting up the Custom function Query(with code to copy)
Grouping the text
Running the Key Phrase Extraction by calling the custom function.
Extracting the key phrases from the returned Json file.
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Check out this demonstration of IBM SPSS Text Analytics for Surveys to help you get up and running quickly with your free trial.
Learn more about IBM SPSS http://ibm.co/spsstrial
Subscribe to the IBM Analytics Channel: https://www.youtube.com/subscription_center?add_user=ibmbigdata
The world is becoming smarter every day, join the conversation on the IBM Big Data & Analytics Hub:
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This episode of Fresh Machine Learning is all Tone Analysis. Tone analysis consists of not just analyzing sentiment (positive or negative), but also analyzing emotions as well as writing style. There are a lot of dimensions to tone, and in this episode I talk about what I consider to be 3 seminal papers in this field. At the end of the episode, we use IBM’s Watson Tone Analyzer API to build our own tone analysis web app.
The demo code for this video can be found here:
https://github.com/llSourcell/Tone-Analyzer
I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/
I introduce three papers in this video
Convolutional neural networks for sentence classification:
http://emnlp2014.org/papers/pdf/EMNLP2014181.pdf
Text categorization using LSTM for region embeddings:
http://arxiv.org/pdf/1602.02373v2.pdf
Hierarchical attention networks for document classification:
https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf
More info about the IBM Watson Tone Analyzer API:
http://www.ibm.com/watson/developercloud/tone-analyzer.html
Some great notes, slides, and practice problems for NLP:
http://cs224d.stanford.edu/syllabus.html
Live demo of the Watson Tone Analyzer:
https://tone-analyzer-demo.mybluemix.net/
Really great long-form page talking about text classification
http://www.nltk.org/book/ch06.html
I love you guys! Thanks for watching my videos, I do it for you. I left my awesome job at Twilio and I'm doing this full time now.
I recently created a Patreon page. If you like my videos, feel free to help support my effort here!:
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In this text analytics with R tutorial, I have talked about how you can scrap website data in R for doing the text analytics. This can automate the process of web analytics so that you are able to see when the new info is coming, you just run the R code and your analytics will be ready.
Web scrapping in R is done by using the rvest package.
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In this Python Tutorial, we will be learning how to install, setup, and use Jupyter Notebooks. Jupyter Notebooks have become very popular in the last few years, and for good reason. They allow you to create and share documents that contain live code, equations, visualizations and markdown text. This can all be run from directly in the browser. It is an essential tool to learn if you are getting started in Data Science, but will also have tons of benefits outside of that field. Let's get started.
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#Python

If you have questions or comments on the content of this video, please email us at [email protected]
What can we do with our textual survey data? How can we gain deeper insights on what our customers are saying about our company and products beyond simple closed-ended questions? Companies large and small are trying to unlock the insights and answers that are contained in natural language texts. These insights are the key to gaining a competitive edge in today’s marketplace. To learn more, watch a replay of our webinar here. - See more at: http://www.lpa.com/resources/#sthash.6wYOuSKZ.dpuf

"There’s a proliferation of unstructured data. Companies collect massive amounts of news feed, emails, social media, and other text-based information to get to know their customers better or to comply with regulations. However, most of this data is unused and untouched. Natural language processing (NLP) holds the key to unlocking business value within these huge data sets, by turning free text into data that can be analyzed and acted upon. Join this tech talk and learn how you can get started mining text data effectively and extracting the rich insights it can bring. We will also demonstrate how you can build a text analytics solution with Amazon Comprehend and Amazon Relational Database Service.
Learning Objectives:
- Get an introduction to Natural Language Processing (NLP)
- Learn benefits of new approaches to analytics and technologies that help empower better decisions, e.g., NLP, data prep
- Build a text analytics solution with Amazon Comprehend and Amazon Relational Database Service in a step by step demo"

A leading expert in text mining, Professor Min Song of the Department of Library and Information Science at Yonsei University in Seoul, Korea focuses his research on the discovery of knowledge from large natural language data such as social media channels, doctor’s notes, and scientific publications. Professor Song discusses his current research interests and new initiatives in a one-on-one interview.
To read more about his research, please visit our website at https://www.yonsei.ac.kr/en_sc/research/archive-view.jsp?article_no=168739&board_wrapper=%2Fen_sc%2Fresearch%2Farchive.jsp&pager.offset=0&search:search_key:search=article_title&search:search_val:search=text&board_no=584&title=contributing-to-a-better-society-with-text-mining
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Medical Record Text Analytics for Health Plans - Delivers the capability to read free form text in medical records such as chart notes, operatory notes, physician correspondence, admission notes, discharge notes, consult notes, nuclear medicine reports, pathology lab reports, and so forth to discover both content and context, then analyze the results and transform those findings into actionable, context based structured data. This data can then be used to provide a dashboard to streamline medical review of claims pended for additional information and be loaded into a data warehouse and combined with existing claims data to enhance insight into medical management, provider quality, pay for performance, wellness programs, etc.. As part of the process, the solution can also be used to assign Snomed CT, LOINC, ICD, & CPT/HCPCS coding to free form text in medical records.
Randall Wilcox
IBM Text Analytics Group: Health Care Principle
[email protected]

This video is a part of the webinar "What is new in KNIME 2.10" July 2014.
It describes the changes introduced in the TextProcessing and in the Network extension::
- Topic Extractor node
- Hierarchy Extractor node
- Additional Tree Layouts in the Network Viewer node
The full webinar video is available at http://youtu.be/jHOUMbKjum8

Mozenda strives to make the internet your database. Much of the data out there is in HTML pages, but some valuable data is locked away in PDF or spreadsheet files. Mozenda has provided tools to extract data directly from files on the internet.

Text Mining with Node.js - Philipp Burckhardt, Carnegie Mellon University
Today, more data is accumulated than ever before. It has been estimated that over 80% of data collected by businesses is unstructured, mostly in the form of free text. The statistical community has developed many tools for analyzing textual data, both in the areas of exploratory data analysis (e.g. clustering methods) and predictive analytics. In this talk, Philipp Burckhardt will discuss tools and libraries that you can use today to perform text mining with Node.js. Creative strategies to overcome the limitations of the V8 engine in the areas of high-performance and memory-intensive computing will be discussed. You will be introduced to how you can use Node.js streams to analyze text in real-time, how to leverage native add-ons for performance-intensive code and how to build command-line interfaces to process text directly from the terminal.

This video shows how to code text documents. It shows coding with codes that emerge from the text (Open Coding), codes named with the selected text segment (In Vivo Coding), and codes already created and stored in a list (List Coding). Nine minutes and 3 seconds.

23-minute beginner-friendly introduction to data mining with WEKA. Examples of algorithms to get you started with WEKA: logistic regression, decision tree, neural network and support vector machine. Update 7/20/2018: I put data files in .ARFF here http://pastebin.com/Ea55rc3j and in .CSV here http://pastebin.com/4sG90tTu Sorry uploading the data file took so long...it was on an old laptop.

PyData Amsterdam 2018
There is an abundance of easily mineable text data (Whatsapp, twitter, and even our own e-mails!), and we have no excuse to not analyse it. In this workshop, we will learn some tips and tricks to deal with messy text data, before moving on to some lesser looked at text analysis techniques, such as text summarisation, working with distance metrics, and an old personal favorite - topic models.
Slides: https://github.com/bhargavvader/personal/tree/master/notebooks/text_analysis_tutorial
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PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.
PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.

In this Whiteboard Walkthrough Ted Dunning, Chief Application Architect at MapR, explains in detail how to use streaming IoT sensor data from handsets and devices as well as cell tower data to detect strange anomalies. He takes us from best practices for data architecture, including the advantages of multi-master writes with MapR Streams, through analysis of the telecom data using clustering methods to discover normal and anomalous behaviors.
For additional resources on anomaly detection and on streaming data:
Download free pdf for the book Practical Machine Learning: A New Look at Anomaly Detection by Ted Dunning and Ellen Friedman https://www.mapr.com/practical-machine-learning-new-look-anomaly-detection
Watch another of Ted’s Whiteboard Walkthrough videos “Key Requirements for Streaming Platforms: A Microservices Advantage” https://www.mapr.com/blog/key-requirements-streaming-platforms-micro-services-advantage-whiteboard-walkthrough-part-1
Read technical blog/tutorial “Getting Started with MapR Streams” sample programs by Tugdual Grall https://www.mapr.com/blog/getting-started-sample-programs-mapr-streams
Download free pdf for the book Introduction to Apache Flink by Ellen Friedman and Ted Dunning https://www.mapr.com/introduction-to-apache-flink

Our Excel training videos on YouTube cover formulas, functions and VBA. Useful for beginners as well as advanced learners. New upload every Thursday.
For details you can visit our website:
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Often many statistical documnets are avaiable to us in PDF format. The PDF file format has many advantages:
1. Adobe Acrobat Reader is free and you can view the PDF files on any computer as long as you have this reader.
2. PDF files often take less space on the computer
3. Viewing PDF files in different magnifications makes it more readable
4. Nowadays it's easy to convert a word document into a PDF file using MS Word
5. Actually PDF has become the format of choice for distributed documents online
However, if you wish to perform calculations and analysis on the numerical data in a PDF file you can either retype the data (time consuming) or use some interesting free or paid tools to convert the data into Excel.
You can subscribe at the following for a paid conversion: https://www.acrobat.com/exportpdf/en/convert-pdf-to-excel.html
$19.99/year
This website http://www.zamzar.com/convert/pdf-to-xls/ offers free PDF to XLS conversion.
Another tool you can try is available at convertpdftoexcel.net.
Get the book Excel 2016 Power Programming with VBA: http://amzn.to/2kDP35V
If you are from India you can get this book here: http://amzn.to/2jzJGqU